Deep Learning Software Market will exhibit a CAGR of 41.3 % | 2030
The Global Deep Learning Software Market size is estimated to be USD 5.98 billion in 2019 and is predicted to reach USD 268.08 billion by 2030 with a CAGR of 41.3 % from 2020-2030. Deep Learning Software is software which is based on artificial intelligence which is a set of algorithms used by machine to learn by itself. It further helps in business development in a long run and brings benefits to the organization. This software can automate things twin new customer and analyze products which is different from the conventional machines and software and can open new approach to the existing business problems.
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There have been increase in globalization and as the changes takes place there have been rapid development in the technology and infrastructure over the forecast period. Furthermore, increased use of artificial intelligence and IoT coupled with increased use of smart devices are further expected to augment the growth of deep learning software market in the near future.
The other factors include increased automation in business and increase in trade practices around the globe and increase in the demand of productivity and efficiency of the overall business are anticipated the market growth. However, complex algorithms used in the deep learning software coupled with lack of technical expertise are expected to inhibit the growth of deep learning software. On the other hand, increase in research and development activities coupled with increased adoption of cloud services and evolving computation technology is further expected to create ample opportunities in the deep learning software market in the near future.
Market Segmentations and Scope of the Study:
The global market share is analyzed on the basis of component, application, end user and geography. Based on component, the market is segmented into service and software. On the basis of application, the market is divided into Image Recognition, Signal Recognition, and Data Mining. On the basis of end user, the market is divided into Security, Marketing, Healthcare, Fintech, Automotive, and Law. Geographic breakdown and analysis of each of the aforesaid segments includes regions comprising North America, Europe, Asia-Pacific, and RoW.
Geographical Analysis
North America is expected to hold the major market share over the forecast period owing to the increased adoption of deep learning software coupled with increase in technological advancement and development of infrastructure. Furthermore, increase in cloud services and globalization leading to increase in trade practices are expected to further augment the growth of market.
Asia Pacific is expected to show significant growth with constant increase in market share owing to the increased demand of automation in business due to the increase in productivity and efficiency. Furthermore, increased use of smart devices coupled with increased use of IoT an artificial intelligence with constant increase in the use of smart devices are expected to expand the market over the forecast period.
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The deep learning software is highly competitive and also consist various market players. Some of the major market players include Adapteva, Inc., Advanced Micro Devices Inc., Facebook, Amazon Inc., Intel Corporation, Sensory Inc., Qualcomm Technologies Inc., Microsoft Corporation, Google LLC, Xilinx Inc., NVIDIA Corporation, IBM Corporation, Samsung Electronics Co Ltd, Baidu Inc, Micron Technology, Mellanox Technologies, and Tenstorrent among others.
There have been various developments taken place over the period of time and are expected to takes place in near future which together contribute the growth of deep learning software market. For instance in January 2019, Amazon has launched the Amazon SageMaker Neo, a new machine learning feature that one can use to train a machine learning model once and then run it anywhere in the cloud and at the edge. They have also released the code as the open source Neo-AI project under the Apache Software License. This release enables processor vendors, device makers, and deep learning developers to rapidly bring new and independent innovations in machine learning to a wide variety of hardware platforms.
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